Australian National University

Master of Applied Data Analytics

  • Delivery: Face to Face
  • Study Level: Postgraduate
  • Duration: 18 months
  • Course Type: Master's

Familiarize yourself with best practices in data analytics.

Course overview

Please note that some of the courses specified in the Master of Applied Data Analytics program will not be offered in intensive mode in 2024 and, depending on enrolments, may not be offered in intensive mode in 2025 either. Students must complete the approved alternative courses listed below on campus, in standard semester schedules, which may include in-person examinations and tutorials.

  • COMP8910 Data Mining (Intensive) replaced with COMP8410
  • COMP8930 Data Wrangling (intensive) replaced with COMP8430
  • COMP7240 Introduction to Database Concepts (intensive) replaced with COMP6240
  • COMP7230 Introduction to Programming for Data Scientists (intensive) replaced with COMP6730
  • COMP6990 Document Analysis (intensive) replaced with COMP6490
  • COMP8920 Neural Networks, Deep Learning and Bio-inspired Computing (intensive) replaced with COMP8420

The Master of Applied Data Analytics is a 1.5-year full-time (or equivalent part-time) degree that provides students with:

  • Exposure to best practice in data analytics.
  • Cutting-edge courses in areas of relevance to data analytics practitioners.
  • An opportunity to deepen knowledge in one of the three areas of computation, statistics, or social science.
  • Professional development for practising data analytics professionals.
  • The opportunity to undertake research of professional relevance.

The program is taught in semester mode and for domestic students, the program is also offered in intensive blended mode. Students studying in an intensive blended mode are expected to be enrolled part-time. The intensive blended course delivery mode is designed to suit working students who take leave from work (or other commitments) to attend an intensive 1 week of full-time learning on campus in the middle of the course and study remotely for the rest of the course. The intensive blended course delivery mode comprises: 4 weeks of online study, one full-time week of face-to-face learning on campus and a further 4 weeks of online study.

Key facts

Delivery
Face to Face
Course Type
Master's
Duration
More Information
Can be studied part time
18 months (Full time)
Price Per Unit
From $4,990.6
More Information
The estimated per units fees are calculated based on the annual average first year fee. It is is based on the standard full-time enrolment load of 48 units per year (unless the program duration is less than 48 units).
Campus
Acton
Intake
New start dates announced soon
Units
12
Fees
More Information
FEE-HELP loans are available to assist eligible full-fee paying domestic students with the cost of a university course.
FEE-HELP

What you will study

The Master of Applied Data Analytics requires the completion of 72 units, which must consist of:

48 units from completion of the following compulsory courses:

  • Data Mining OR Data Mining (Intensive)
  • Data Wrangling OR Data Wrangling (intensive)
  • Introduction to Social Science Methods and Types of Data
  • Using Data to Answer Policy Questions and Evaluate Policy
  • Regression Modelling
  • Generalised Linear Models
  • Graphical Data Analysis
  • Introductory Statistics for Business and Finance

Six units from completion of courses from the following list:

  • Relational Databases
  • Introduction to Database Concepts (intensive)

Six units from completion of courses from the following list:

  • Programming for Scientists
  • Introduction to Programming for Data Scientists (intensive)

12 units from completion of courses from any of the following lists:

Computer Science

  • Statistical Machine Learning
  • Computational Methods for Network Science
  • Document Analysis OR Document Analysis (intensive)
  • Neural Networks, Deep Learning and Bio-inspired Computing OR Neural Networks, Deep Learning and Bio-inspired Computing (intensive)

Social Science

  • Social Research Practice
  • Online Research Methods
  • Advanced Techniques in the Creation of Social Science Data
  • Advanced Social Science Approaches to Inform Policy Development and Service Delivery

Statistical Data Analysis

  • Introduction to Bayesian Data Analysis
  • Principles of Mathematical Statistics
  • Statistical Learning
  • Applied Time Series Analysis

Entry requirements

Applicants must present one of the following:

  • Bachelor's degree with honours or international equivalent with a minimum GPA of 5.0/7.0.
  • Bachelor's degree or international equivalent with a minimum GPA of 5.0/7.0, plus at least three years of relevant work experience.

The GPA for a Bachelor program will be calculated from (i) a completed Bachelor degree using all grades and/or (ii) a completed Bachelor degree using all grades other than those from the last semester (or equivalent study period) of the Bachelor degree. The higher of the two calculations will be used as the basis for admission.

Ranking and English Language Proficiency

All applicants must meet program-specific academic/non-academic requirements and English language requirements. Admission to most ANU programs is competitive, so meeting all admission requirements does not automatically guarantee entry.

In line with the University's admissions policy and strategic plan, an assessment for admission may include competitively ranking applicants based on specific academic achievement, English language proficiency and diversity factors. Applicants will first be ranked on a GPA ('GPA1') calculated using all but the last semester (or equivalent) of the Bachelor's degree used for admission purposes. If required, ranking may further be confirmed based on:

  • GPA ('GPA2') calculated on the penultimate and antepenultimate semesters (or equivalent) of the Bachelor's degree used for admission purposes.
  • And/or demonstrating higher-level English language proficiency.

All students who gain entry will have their Bachelor's degree reassessed prior to enrolment in this ANU program to confirm that the minimum requirements were met.

Please reach out to the university for more information.

Outcomes

Learning outcomes

  • Select, adapt, apply and communicate advanced data analytics methods and techniques.
  • Apply data analytics to policy, business and service delivery decision-making.
  • Examine current issues in data analytics using leading-edge research and practices in the field.
  • Demonstrate strong cognitive, technical and communication skills to work independently and collaboratively to collect, process, interpret and communicate the outcomes. of data analytics problems.
  • Communicate complex data analytics outcomes to diverse audiences.

Fees and FEE-HELP

Annual indicative fee in 2025: $39,925 (domestic full-fee paying place)

The annual indicative fee for a program is based on the standard full-time enrolment load of 48 units per year (unless the program duration is less than 48 units).

A student’s annual fee may vary in accordance with:

  • The number of units studied per term.
  • The choice of major or specialisation.
  • Choice of units.
  • Credit from previous study or work experience.
  • Eligibility for government-funded loans.

All students are required to pay a services and amenities fee.

Student fees shown are subject to change. Contact the university directly to confirm.

FEE-HELP loans are available to assist eligible full-fee paying domestic students with the cost of a university course.

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